This course introduces students to regression as a tool to answer questions about education. Regression is the most commonly used quantitative method in education research and can be used to answer many causal research questions and descriptive research questions. However, using regression appropriately requires thoughtfulness about what kinds of questions regression can answer, the assumptions regression relies on, the limitations of our data, and how particular variables (e.g., “race” and “gender”) are incorporated into analyses. Otherwise, regression results may be biased and may reify rather than interrogate problematic ideas. Therefore, EDUC250 teaches the fundamentals concepts of regression analysis and how these concepts can be thoughtfully applied to answer different kinds of research questions, with a particular emphasis on explicitly critical research questions. The course also emphasizes how to understand and critically assess research that uses regression. The course integrates theory and application using the R programming language. Students will be assessed through four substantive assignments, including the final, capstone assignment in which students will conduct the major steps in the life-cycle of a regression research project. \ THIS DESCRIPTION IS A BIT OVER WORD LIMIT. I’LL GET DOWN BELOW WORD LIMIT FOLLOWING FEEDBACK
Regression is the most widely-used quantitative methodology to answer causal, and also non-causal, research questions. This section of EDUC152 will introduce students to regression with a focus on using regression to answer causal research questions, which typicaly follow the form “what is the effect of X on Y.” The course also emphasizes undersanding how to read and critically assess empirical research that uses regression.
The course integrates statistical theory and application using the R programming language. Students will work through asynchronous video lectures and lectures slides on their own. These lectures introduces statistical theory, introduces the relevant programming skills, and provides the code and real-world data so that students can practice conducting and interpreting statistical analyses. Course topics will include: fundamental statistical concepts of statistical inference; principles of causal inference; and fundamentals of multiple regression. During class time, students will work in groups to solve practical research challenges and we will discuss and deconstruct empirical research that uses regression analysis. The primary course assessments are four problem sets – including the final capstone problem set – which will be completed in groups. Each problem set will require students to apply knowledge of statistical concepts, and conduct substantive statistical analyses around a particular research question.
The course embraces using regression to answer traditional research questions (e.g., the effect of student-teacher ratio on achievement) and critical research questions (e.g., the effect of racial salience – as presented in email text – on how white university admissions officers respond to inquiries from Black prospective students). The skills this course teaches are valued by employers and are valued in the process of applying to graduate schools. After completing this course, students will be prepared to take more advanced causal inference coursework (e.g., quasi-experimental methods) and coursework that teaches the programming and data manipulation skills necessary to create analysis datasets for real research projects.
Big-picture (conceptual) learning goals
Skill-based learning goals
Each week, the course will be structured around asynchronous (pre-class) lectures and one synchronous workshop-style class meeting per week. Weekly homework will consist of students working through the lectures on their own and a modest amount of required reading. Written homework will consist of four “problem sets.” Students will complete the first three problem sets in groups. Students will complete the final capstone problem set, due during finals week, on their own.
Prior to our in-class meetings, students should work through lecture materials on their own. We recommend treating the lecture materials as an active learning experience, in which students run R code on their computer instead of merely reading text on the slide. Additionally, we recommend that students ask questions on the course github website when they are having difficulty with the material.
With respect to written work, the problem sets – described below – will be substantive and are intended to be challenging. Students who devote time each week working through the lecture materials will be better prepared for the problem sets. We recommend starting the problem sets early. This way students will have plenty of time to ask for help on questions they find challenging.
(Instructor, TA, and Community Expectations)
We will be using GitHub teams for class announcements.
@mentioned by all students enrolled in the class and part of the organization.Credit: Introducing team discussions
We will be using GitHub issues for questions and class discussion.
GitHub issues: GitHub issues are traditionally used by collaborators of a repository for managing tasks for a project. Our rational for using issues is twofold: 1) help track and organize questions related to course material and problem sets and 2) promote classroom participation. Students are encouraged to contribute to issues by posting questions, sharing helpful resources, and/or taking a stab at answering questions posted on issues. Some features include:
TBA, but will chose one or two free books
Links to reading on course website
The primary course assessments are four problem sets, including the final capstone problem set. Each problem set will require students to apply knowledge of statistical concepts, conduct substantive statistical analyses, present and interpret results. Problem sets will also be designed to introduce students to some of the thorny data challenges that inevitably arise in real research projects. The final, capstone problem set will require students to conduct the major components of an empirical regression analysis, from research question and variable collection to modeling, presentation, and interpretation. Additionally, the capstone problem set will require students to critically evaluate an empirical journal article that utilized the same data sources to answer the same research question.
COMMENTS
| Letter Grade | Percentage |
|---|---|
| A+ | 99-100% |
| A | 93-98.9% |
| A- | 90-92.9% |
| B+ | 87-89.9% |
| B | 83-86.9% |
| B- | 80-82.9% |
| C+ | 77-79.9% |
| C | 73-76.9% |
| C- | 70-72.9% |
| D | 60-69.9% |
| F | 0-59.9% |
With respect to the course material, learning the essential skills of programming is hard! This stuff feels overwhelming to me all the time. So it is important that we all create an environment where students feel comfortable asking questions and talking about what they did not understand. Discomfort is part of the learning process. Unburdern yourself from the weight of being an “expert” and just focus on improving, helping your classmates improve, and helping your instructors improve.
With respect to classroom environment, let’s work together to create an environment that is relaxed, supportive, and where students feel comfortable voicing any concerns they have. Be mindful that words and body language affect people. Express your thoughts in a way that doesn’t make people feel excluded and does not make disparaging generalizations about any group. As an instructor, I am responsible for setting an example through my own conduct.
You will communicate with instructors and peers virtually through a variety of tools such as GitHub, email, and web conferencing. The following guidelines will enable everyone in the course to participate and collaborate in a productive, safe environment.
Students needing academic accommodations based on a disability should contact the Center for Accessible Education (CAE) at (310)825-1501 (located in Murphy Hall A255). When possible, students should contact the CAE within the first two weeks of the term as reasonable notice is needed to coordinate accommodations. For more information visit https://www.cae.ucla.edu/.
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Students in distress may speak directly with a counselor 24/7 at (310) 825-0768, or may call 911; located in Wooden Center West; www.caps.ucla.edu
Title IX prohibits gender discrimination, including sexual harassment, domestic and dating violence, sexual assault, and stalking. If you have experienced sexual harassment or sexual violence, you can receive confidential support and advocacy at the CARE Advocacy Office for Sexual and Gender-Based Violence, located on the A-level of Murphy Hall (Room A233). More information is available here: https://www.sexualviolence.ucla.edu/Get-Help. In addition, Counseling and Psychological Services (CAPS) provides confidential counseling to all students and can be reached 24/7 at (310) 825-0768. You can also report sexual violence or sexual harassment directly to the University’s Title IX Coordinator, 2255 Murphy Hall, titleix@conet.ucla.edu, (310) 206-3417. Reports to law enforcement can be made to UCPD at (310) 825-1491.
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